A Method For Determining The Condition Of An Object By Magnetic Resonance Imaging

ABSTRACT

A method for determining the condition of an animate or inanimate object by magnetic resonance imaging, MRI, particularly for determining the pathologic condition of rheumatoid arthritis (“arthritis rheumatoides”) and carrying out a patient follow-up.

FIELD OF THE INVENTION

The present invention relates to a method for determining the conditionof an animate or an inanimate object by MRI.

BACKGROUND OF THE INVENTION

The present invention particularly relates to a method for determiningthe condition of an animate or an inanimate object which condition canbe evaluated by providing information on time dependent effects whichare either induced or spontaneous. For example, in a solid body which isnot of a biological type, the perfusion of a gas or a liquid which isfed under certain condition can be observed by MRI, such as by acquiringa sequence of images in which each image is acquired at a different timewithin a certain time period. Structural modifications relating to achange in physical or chemical parameters can be observed, such as, forexample, temperature dependent structural changes in solid bodies whichare subjected to temperature which varies over time by heating or bycooling or structural changes which are induced by mechanical stressthat is exerted on a body, or structural or chemical changes that areinduced by exposure to radiation in which the intensity or dosagechanges with time, structural or chemical changes which occur due to theapplication of chemical substances to the body that are interacting withthe substance or the material of which the body is made.

Such time dependent effects may be observed by carrying out an MRIsession of the body in which an image is acquired at predetermined timeintervals. In this way, the time dependent changes in parameters of theimage may be empirically determined, for example, time dependent changesof the mean intensity of the image or of only a partial area of theimage.

In many cases, particularly in cases in which MRI is applied fordiagnostic purposes, the pathological condition of in vitro or livingtissues is a time dependent activity or one which can be bestappreciated by using induced mechanisms in the tissue which are timedependent. This means that in order to isolate or to identify thecondition of the object it is necessary or preferable to carry out asequence of images, with each image being acquired at certain differenttimes within a certain time interval.

Particularly relevant is the case for isolating and identifying andevaluating pathologic conditions such as infections and/or tumors, orthe like. It is known that vascularisation is enhanced in regions inwhich tumoral cells are present or in which there is an infection. Thusin this case in order to isolate and identify such conditions it isknown to evaluate the increase of vascularisation by means of contrastmedia perfusion measurements.

Perfusion measurements conducted with MRI are usually carried out byacquiring a sequence of images of a selected anatomical region or tissueregion after a contrast media has been injected into the region.Contrast media are transported by the blood and the speed of transportof diffusion of the contrast media in the tissue or anatomical region ofinterest is used for evaluating vascularisation. The mean intensity ofthe imaged region of interest is then determined from the image data ofeach image of the sequence and a so-called “perfusion curve” isconstructed from the data pairs, the mean intensity of the region ofinterest of each one of the images of the sequence of images, and thetime of acquisition of the images.

By comparing the perfusion curve with other perfusion curves which areuniquely related to known clinic cases or tissue conditions, it is thenpossible to determine some indications on the clinical condition of theimaged tissue or anatomical region.

This evaluation has been carried out until now in a non-systematic way.

Thus there is the need for providing for a method of determining acondition of an object, particularly the pathological condition ofliving or in vitro tissues which is much more reliable and which isintegrated in a well defined manner and which automatically determinesthe indications about the probable pathological condition of an objectunder examination which pathological condition is unknown.

The invention therefore has the aim of providing a method fordetermining the condition of an object by MRI.

The invention is directed to providing a method enabling one to haveunique criteria so that measurements carried out by different subjectsand different devices can be reliable and comparable, thus permitting awidespread exchange of data between different subjects which carry outthe examination and evaluate the results.

Another goal of the present invention is to reduce the uncertaintiesrelated to the skill involved in carrying out such examinations and invisually comparing and interpreting the results.

SUMMARY

The above mentioned goal is achieved by the method the present inventionfor determining the condition of an object by MRI, comprising thefollowing steps:

a) acquiring at least one image of a sample object by MRI along at leastone slice or one section plane of an object under examination atdifferent successive times within a predetermined time period;

b) the sample object having known conditions;

c) determining for each image or for a selected region of each image,which selected region is identical for each image or refers to anidentical part of the imaged sample object under examination, the meanintensity of the MRI signal;

d) constructing an empirical time dependency function of the meanintensity by using data pairs consisting of the mean intensity of eachimage or of the part of each image and the corresponding time ofacquisition;

e) analytically determining the parameter of a function approximatingthe said empirical time dependency function;

f) providing at least a second object to be examined having an unknowncondition and carrying out the steps a) to e) using the second object;and,

g) comparing the parameters of the function which approximates theempirical time dependency function relating to the sample object withthe parameters of the second object in order to detect differences ofthe condition of the second object from the known conditions of thesample object.

Objects being examined which are either of a biological or anon-biological type and/or which are inanimate or animate are often ofthe type which are able to show at least two or more conditions or acontinuously varying range of conditions. In this case, the abovementioned steps may be carried out for every discrete possible conditionof the body under examination or for only a certain number of differentselected conditions of the continuously varying conditions.

The unknown condition of additional objects may be determined bycarrying out the steps described in the precedent paragraph for theseadditional objects and then comparing the parameters of the functionapproximating the empirical time dependency function relating to theadditional objects with the parameters of the function approximating theempirical time dependency function relating to the sample object(s).

Different mathematical or statistical tools may be applied for carryingout the comparison of the parameters of the function approximating theempirical time dependency function relating to additional objects withthe parameters of the function approximating the empirical timedependency function, i.e. in order to determine the condition of theexamined objects, which condition is unknown.

Having a range of discrete conditions relating to the sample objects,the condition of the object under examination may be determined byinterpolation.

A different approach may consist of generating a database comprisingtime dependent functions of the sample objects relating to theircondition and to the corresponding known condition and using apredictive algorithm such as a neural network for determining thecondition of the object under examination on the basis of the timedependent mean intensity function obtained by acquiring the sequence ofMRI images.

When the objects do not show time varying effects which produce timevarying image parameters, particularly to time varying intensities ofthe images of the sequence of MRI images, the time varying parameterscan be induced or forced by applying to the sample objects and to theadditional objects (for which the condition has to be determined) amedium which is able to diffuse within the objects and then carrying outthe acquisition steps of one or more images of the objects along one ormore selected slices or section planes within a time period startingbefore or at or immediately after the application of the diffusion mediaand ending after the complete diffusion of the media in the object.

In order to select the slice or slices along which the sequence ofimages has to be acquired, a first panoramic image (a so-called “scoutimage”) is acquired along a first scout slice or section plane by whichone or a series of selected section planes across the objects aredetermined and along each of which an image has to be acquired.

Furthermore, when only a limited and known region of the images alongthe selected slice or slices has to be examined or is relevant fordetermining the condition, a first image along a first section plane isacquired and then the limited region of the image is determined (whichregion is a so-called “region of interest” or “ROI”), the geometricshape and position of the region of interest being determined andselected on each further image which is acquired at later times alongthe same slice or section plane.

Determining the region of interest is particularly relevant in the casein which the object to be examined is an anatomical region of a patient,either human or animal.

The present invention can be further improved by noting that instead ofacquiring images along one or more section planes or slices, athree-dimensional image of the sample objects and of the objects havingunknown conditions (or of a part thereof) may be acquired, thuscollecting a sequence of three-dimensional images which are eachacquired at different times within a certain time period.

Acquiring a sequence of three-dimensional images which can be stored asvolumetric image data allows one to define and select section planes andregions of interests at a later time, without running the risk of havingto repeat the acquisition process due to an imprecise or incorrectselection of the slices and/or of the regions of interest.

After having acquired the sequence of three-dimensional MRI images oralternatively in the acquisition process in a first three-dimensionalimage, one or more three-dimensional partial regions are selected, saidregions being so-called three dimensional regions of interest, and theposition in space and boundaries of the regions of interest beingdetermined, while the regions of interest are automatically selected foreach subsequent three-dimensional image being acquired at later times,the mean intensity of the image data of each three-dimensional region ofinterest being calculated from the image data and used for constructingan empirical time dependency function of the mean intensity of theregion of interest by the data pairs consisting of the mean intensity ofeach three-dimensional region of interest of each three-dimensionalimage of the sequence of three-dimensional images and the correspondingtime of acquisition.

It is also possible not to limit examination to only one region ofinterest. In this case, two or more selected regions of the sequence oftwo-dimensional images along one or more selected slices or sectionplanes of the objects or of the sequence of three-dimensional images ofthe objects are defined and an empirical time dependency function of themean intensity is constructed separately for each different selectedregion by the data pairs consisting of the mean intensity of thecorresponding selected region of each corresponding two- orthree-dimensional image of the sequence of two- or three-dimensionalimages and the corresponding time of acquisition.

A further alternative or improvement consists in the fact that two ormore selected regions of the sequence of two-dimensional images alongone or more selected slices or section planes of the objects or of thesequence of three-dimensional images of the objects are defined and anempirical time dependency function of the mean intensity is constructedin which for each two-dimensional image along one selected plane orsection plane of the sequence of section planes or for eachthree-dimensional image of the sequence of three-dimensional images themean intensity is determined by the sum of the mean intensities of thedifferent selected region of interests.

When limiting imaging to the two-dimensional case, the images along oneor more selected slices or section planes are acquired through athree-dimensional image of the objects;

defining within the three-dimensional image one or more planes crossingthe volume represented by the three-dimensional image; and,

reconstructing the image along the one or more planes from thethree-dimensional image data by selecting the image data falling on eachselected plane crossing the volume.

In order to have comparable data from different objects examined, it isimportant that the region of interest being chosen corresponds to theidentical region of the real object. In the case of diagnostic images,this means that the region of interest for each object should correspondto the same anatomical region for each object and sample object beingexamined. In order to ensure this, the invention provides markers whichare applied to the objects in uniquely defined positions, with themarkers generating uniquely recognizable image data in the images beingacquired and with the selected region or regions being defined by thegeometrical relation of the markers with the position and boundaries ofthe selected regions.

As an alternative to external markers which are positioned in welldefined points of the object, markers can also be used which are definedas selected zones or regions of the objects which are uniquelyidentifiable in the images of the selected slices or section planes orin the three-dimensional images.

For each slice image of the sequences of slice images or for eachthree-dimensional image of the sequence of three-dimensional images, theselected region of interest or the selected regions of interest may beuniquely identified by their known geometrical form and from thegeometrical relation (distance and orientation) relative to the markers.This operation can be carried out by use of a simple mathematicalalgorithm such as, for example, a so-called “registrationalgorithm”which is well known in the art (see for example Hemmendorf,M.; Anderson, M. T.; Kronander, T.; Knutsson, H. Phase-basedmultidimensional volume registration, IEE Trans Med Imaging 2002, 21,1536-43).

A particular field of use of the above-mentioned invention is the fieldof diagnostic imaging. The above method can be used for determiningwhether, on the basis of MRI images of an anatomical region of apatient, a pathological condition is present in that region and, if itis present, the above-mentioned method can provide as a furtherimprovement also an evaluation of the level or stage of the pathologicalcondition.

Particularly in the case of infections and/or of tumors, the presentinvention comprises carrying out the method by acquiring a sequence ofimages of the anatomical region, which images are taken at certain timesduring a certain period with that period lasting long enough to allow acomplete perfusion of the anatomical region by a contrast media which isinjected in that anatomical region.

A particular kind of disease or illness suitable for examination usingthe present invention could be rheumatoid arthritis (or “arthritisrheumatoides”). As an important region of interest to which imaging hasto be carried out, the sinovial can be used. It has been shown that thisanatomical region is representative for identifying the stage or levelof activity of the disease.

Furthermore the invention can be used for carrying out a follow-up, thushelping to recognize the way that the pathological condition develops orthe way that the said pathological condition is regressing due, forexample, to medical treatment.

Thus, for the above purposes, the invention relates to a method forcarrying out a follow-up of the pathological conditions of biologicaltissues in isolated form or in or of an anatomical region of a bodycomprising the steps of:

a) acquiring at least one image by MRI along at least one slice or alongone section plane or a volume or a selected three-dimensional regionwithin the volume of the biological tissue(s) having a known conditionat different successive times within a predetermined time period;

b) determining the mean intensity of the MRI signal for each image orfor a selected region of each image, which selected region is identicalfor each image or refers to an identical part of the imaged samplebiological tissue(s) under examination;

c) constructing an empirical time dependency function of the meanintensity by using the data pairs consisting of the mean intensity ofeach image or of the part of each image and the corresponding time ofacquisition;

d) analytically determining the parameter of a function approximatingthe empirical time dependency function;

e) providing at least a second biological tissue to be examined havingan unknown condition and carrying out the steps a) to d) using the saidsecond biological tissue;

f) comparing the parameters of the function approximating the empiricaltime dependency function relating to the sample biological tissue(s) andto the second biological tissue(s) having an unknown condition in orderto detect differences of the condition of the second object from theknown conditions of the sample object.

Considering the use of the present invention for a follow-up of thedisease, the invention has the following steps:

repetition, at several different times, of the steps e) and f) fordetermining changes in the degree of disease activity over time andduring a therapeutic treatment.

When the above method is applied in combination with the presence ofcontrast media in the examined object or in the region of interest ofthe examined object, the follow-up of the disease activity of apathological condition of the object and within the region of interestcomprises the steps of:

a) generating a database of contrast media perfusion curves each oneuniquely associated to a well-defined degree of disease activity;

b) the database being generated by acquiring at least one image of anidentical region of interest of the same anatomical region by MRI alongat least one slice or one section plane of the anatomical region inpatients or a three-dimensional MRI image of the anatomical region inmore than one patient, each patient having a known degree of diseaseactivity;

c) for each patient having a well-defined degree of disease activity,acquiring a sequence of MRI images which sequence comprises a certainnumber of MRI images taken at different times, one from the other,within a certain period of time;

d) the period of time starting immediately after or at the injection ofa contrast medium in the anatomical region and terminating after acertain time which is determined as a typical duration of contrast mediaperfusion in the anatomical region;

e) determining for each image or for a selected region of each image,which selected region is identical for each image or refers to anidentical part of the imaged anatomical region under examination, themean intensity of the acquired image data;

f) constructing an empirical time dependency function of the meanintensity by using the data pairs consisting of the mean intensity ofeach image or of the part of each image and the corresponding time ofacquisition;

g) analytically determining the parameter of a function approximatingthe empirical time dependency function;

h) determining the disease activity in the same region of interest ofthe same anatomical region of a patient having an unknown level ofdisease activity by carrying out the steps of injecting the contrastmedia in the anatomical region of the patient and by acquiring thesequence of MRI images of the anatomical region for the predeterminedperiod of time and finally constructing the empirical time dependencyfunction of the mean intensity by using the data pairs consisting of themean intensity of each image or of the part of each image and thecorresponding time of acquisition and analytically determining theparameter of a function approximating the empirical time dependencyfunction;

i) comparing the parameters of the function approximating the empiricaltime dependency function relating to the database in order to detect thedisease activity level; and,

j) repeating at several different times the steps h) and i) fordetermining changes in the degree of disease activity over time and/orduring a therapeutic treatment.

The above mentioned method allows one to estimate the health conditionof a patient and the time development of disease with or withouttherapy.

It has been found that the above mentioned method allows one todetermine the level of disease activity and the health condition ofrheumatoid arthritis in patients by MRI perfusion measurements of thewrist and particularly of the synovial membrane. Indeed, the synovialmembrane is the anatomical site where early inflammation can bedetected. The method allows one to discriminate an active disease froman inactive disease and the level of activity.

In this case, the anatomical region under examination is the wrist andthe region of interest is the synovial membrane.

Since the measurements relate to determining a mean intensity of theimage along a selected region which is the entire region of interest ora selected part thereof and since the follow-up requires that imaginghas to be carried out in different sessions at different times, it isimportant and critical that at each session the same slice or region ofinterest is selected. This can be very difficult to perform and canprovide errors since the correct selection depends on the positioning ofthe wrist in the MRI scanner. Thus, using a three-dimensional MRIacquisition method and considering the mean intensity of an entirethree-dimensional region of interest or of a three-dimensional partthereof, can be helpful in avoiding errors due to improper positioningand thus to the selection of an incorrect section plane or an incorrectregion of interest. Further providing markers either of the externalkind or consisting in precise anatomical or morphological particulars ofthe anatomical region under examination, and combining the markers withthe above-mentioned registration methods and algorithms allows one toenhance the precision of the method also in combination with thethree-dimensional MRI acquisition.

Further improvements of the present invention are described in thedependent claims.

BRIEF DESCRIPTION OF THE DRAWING FIGURES

The present invention and the advantages deriving therefrom will appearmore clearly from the following description of some illustrativeexamples and of the annexed drawings and tables, in which:

FIG. 1 shows a diagram explaining the basic steps of the methodaccording to the present invention relative to the sequence of MRIacquisitions carried out for a contrast media perfusion measurement andrelative to the case using three-dimensional MRI;

FIG. 2 shows an example of a typical perfusion curve obtained byreporting the mean intensity of the region of interest of images takenat different times with respect to the time of acquisition;

FIG. 3 is a table in the form of a flux diagram explaining the methodsteps of the invention according to a first embodiment;

FIG. 4 is a variant of the embodiment according to FIG. 3 where apredictive algorithm is used for evaluating the condition of theexamined object;

FIG. 5 illustrates a table explaining the basic steps of a diseasefollow-up according to the present invention; and,

FIGS. 6A to D illustrate an example of the use of markers in order toidentify the same section plane along which the MRI image has to beacquired independently of the position of the object under examinationin the MRI scanner, which in this case is a hand.

It is to be stressed that while the examples on which the followingdescription is based relate to a biological case in which the objectunder examination is the wrist of a living patient, the method is notlimited to this example.

Examinations of the same kind can be carried out on animate andinanimate bodies or on in vitro tissues. Furthermore, the present methodcan be applied at least relatively to its basic steps on non-biologicalobjects. For example, on inanimate materials, where instead of theso-called contrast media, the perfusion or transition of waves or offluids such as liquids or gas or particles modifying their structure maybe used, especially where the modifications induced by the said mediaare visible by MRI.

DETAILED DESCRIPTION OF PREFERRED EMBODIMENTS OF THE INVENTION

Relating to FIG. 1, an object 1 is represented by a cube. The object 1is positioned in an MRI scanner and one or more panoramic images areacquired, the so-called “scout images”. If needed, these images are usedby the operator to select a certain particular region of the object 1, aso-called “region of interest” (ROI) indicated by a small cube 101within the object 1.

Thus, the apparatus is ready to carry out a sequence of images of thesame region of interest 101 at different times.

FIG. 1 illustrates the sequence of imaging acquisitions along a timeaxis t by reproducing the image of the object 1 and of the region ofinterest 101 at each acquisition time ti, where i=0,1,2,3, . . . ,n.

The object 1 under examination can show spontaneous time varying stateswhich are typical for a certain condition of the object. If this is notthe case, a time varying state can be induced in the object underexamination 1 and more precisely in the region of interest by subjectingthe object to a treatment. Such treatment can be, for example, theinjection of a fluid such as a gas or a liquid which is able to permeatethe body under examination or the application of a mechanical wave or ofan electric or electromagnetic wave or signal.

A typical case in the examination of biological tissues is the injectionof contrast media. This is indicated schematically by the referencenumeral 2 in FIG. 1. Contrast media are transported by the vascular orlymphatic system and their concentration varies over time from theinstant of injection. Contrast media are known and give a very high andidentifiable MRI signal. By repeating the MRI image acquisition severaltimes at different times from injection of the contrast media in theobject under examination, the quantity of contrast media in the regionof interest 101 first increases and then roughly reaches a maximum whichis maintained for a certain time. In the MRI images of the sequence,this can be detected by examining the mean intensity of each image ofthe sequence. Reporting these values in relation to the time ofacquisition, allows one to draw a so-called perfusion curve, an exampleof which is illustrated on FIG. 2. The stars indicate the measuredvalues of the mean intensity of each MRI image of the region of interest101 at the time of their acquisition. The curve passing through thestars is an interpolation curve which represents a function of the meanintensity with respect to time.

These kinds of measurements are known as perfusion measurements and areused in MRI diagnostic and in ultrasound diagnostic.

In diagnostic use, perfusion curves give a measure of vascularisation ofa tissue or anatomical region which is a sign of the presence of anabnormal condition. Increased vascularisation can be observed in thepresence of inflammations, infections and also tumors. Thus perfusionmeasurements can be of help in determining a pathological condition of apatient.

If non-biological material is considered, such as, for example,permeable materials, then a fluid or a gas can be applied with a certainpressure to the permeable material and then the perfusion curve of thefluid can be determined in order to evaluate the homogeneity ofpermeation within the entire cross section and length of the materialand/or whether the permeability has to follow a certain direction offlux deviations, can be observed.

In order to be able to evaluate the condition of an object by the methodaccording to the present invention, a certain number of sample objectshaving a known condition have to be submitted to the perfusionmeasurements, as indicated by the diagram of FIG. 3. In this case,perfusion measurements according to the above description are carriedout for each of the n sample objects and the so determined empiricalperfusion functions f1 to fn are determined. These functions can beuniquely related to the known conditions C1 to Cn of each of the said nsample objects.

Each empirical function f1 to fn can then be approximated by a functionas, for example, a polynomial expansion or series in which parametersP(f1) to P(fn) are uniquely correlated to the conditions C1 to Cn of thesaid n sample objects. Algorithms which are capable of carrying out thisstep are known and widely used by persons skilled in the art, since thealgorithms are within the common, general knowledge.

Thus a database has been constructed which comprises data vectorsconsisting of the parameters P(f1) to P(fn) and the correspondingcondition for the sample object.

The database further comprises the definition of the region of interest101 which is the same for each sample object and which corresponds tothe same part in each sample object.

The method according to the present invention further provides the stepsof carrying out the perfusion measurement of the same region of interest101 which is located at the same part of an object to be examinedregarding an unknown condition. This is shown at the left column of FIG.3.

Imaging and the determination of the perfusion curve fE and thedetermination therefrom of the parameters P(fE) is carried out inexactly the same manner as disclosed above for the sample objects andwith reference to FIGS. 1 and 2.

A comparison step of the parameters P(fE) with the database allows oneto identify at least the closest condition C1 to Cn to which thecondition CE of the object under examination for an unknown conditionexists. Different cases may be possible. If one considers that theobjects may have only discrete conditions, then no interpolation isnecessary. If the conditions of the objects may vary continuously, thenthe condition of the examined object having an unknown condition may befurther determined by interpolation when the parameters P(fE) fallsbetween two parameters of the sample objects.

Also when carrying out MRI of the object under examination for anunknown condition, it is important to determine the same region ofinterest 101 located at the same part of the object as in the sampleobjects.

Although the present invention can be carried out considering imagingalong one or more selected slices, i.e., section planes of the objects,thus being limited to a so-called two-dimensional MRI case. The bestresults can typically be achieved by using three-dimensional MRI.

Three-dimensional imaging techniques are known and allow one to acquirea three-dimensional array of image data. (See, for example, “MagneticResonance Imaging, Physical Principles and Sequence Design” E. MarkHaacke, Robert W. Brown, Michael R. Thompson, Ramesh Venkatesan JohnWiley & Sons Inc. Publication,; “Practical NMR Imaging” M. A Foster & J.M. S. Hutchinson IRL Press). In this case, uncertainty, relating to thefact that for each object the same slice or slices or the same sectionplane or section planes are selected due to different positioning of theobjects relative to the MRI scanner, is widely reduced.

Three-dimensional MRI allows one also to carry out the method by usingslice images, since once a three-dimensional image data array has beenacquired, a section plane or slice of the imaged volume can be definedand the image data falling on the said slice can be selected andretrieved from the image data memory.

Nevertheless, as said above, choosing to use a three-dimensional regionof interest 101 allows one to improve precision.

In order to further enhance precision, either in the case oftwo-dimensional MRI or of three-dimensional MRI, markers can be providedon the objects under examination.

These markers can be external markers which are applied on the objectsat the same places relative to the shape of the objects. This can bedone by identifying morphologically or anatomically unique points on theobjects surface.

Markers provide for uniquely identifiable MRI signals which can be usedto bring into register the images acquired from each different object.Register algorithms capable of carrying out this task are known. Onesuch algorithm and the corresponding method are disclosed in Hemmendorf,M.; Anderson, M. T.; Kronander, T.; Knutsson, H. Phase-basedmultidimensional volume registration. IEE Trans Med Imaging 2002, 21,1536-43.

FIGS. 6A to 6D explain, with a simplified example, the effects of suchcombination of markers and registration algorithms.

FIGS. 6A to 6D show schematic views of different MRI sessions carriedout at different times. Each time positions the object, which in thiscase is a hand, in the scanner and each time defines an imaging volumeenclosing the hand. Markers are provided at selected identical positionson the hand for registering a slice image in order to identify at eachimaging session the same section plane across the hand along which aslice image has to acquired and displayed.

In FIGS. 6A to 6D, the volume V defined by the user and in which theimage data has to be acquired is represented by a rectangle. Assuming,for simplicity, that this volume is always the same at each imagingsession, the hand can be positioned differently at each session relativeto the volume with respect to all the other sessions. Thus, sectionplane P1A, defined in the session represented by FIG. 6A, willcorrespond if referred to the hand as a body under examination tosection plane P1B, P1C and P1D in the following imaging sessionsrepresented by FIGS. 6B to 6D.

In order to identify the correct section plane, at least one andpreferably two or more markers can be provided. The markers can be, asillustrated in FIG. 6A to 6D, MRI opaque elements 30 which can bepositioned on the body under examination at uniquely and repeatedlyidentifiable points of the anatomy or shape of the body.

Alternatively, the markers can be parts or zones of the anatomy of thebody under examination which are uniquely recognizable as particularlyevident zones and which are constant. This allows one to use these zonesas intrinsic markers.

Combination of these anatomic markers and of the opaque elements canalso be used.

The example is referred to the identification of a section plane withina volumetric image data but the same method applies also when athree-dimensional region of interest 101 has to be identified andcorrectly oriented relative to the object to be imaged.

The markers can be searched and identified within each of the volumetricimage data acquired at each imaging session. The markers can be used tospatially align the volumetric image data of a predefined region ofinterest 101 by applying a so-called “registering algorithm” asdiscussed above.

The effect of the registration algorithm is that, referring to theexample of FIGS. 6A to 6D, the plane P1 A can be correctly repositionedrelative to the imaged object (namely the hand) in the image data ofsubsequent imaging sessions by giving to it the correct orientation asindicated by P1B, P1C, P1D in FIG. 6B, 6C, 6D. The same applies in thecase of a volumetric region of interest 101.

The position of the object to be imaged, namely the hand, can beidentified and displacement vectors can be determined with reference tothe position of the hand in FIG. 6A, which displacement vectors can beused by calculating the new position and orientation parameters of thesection plane, such as the section plane P1 A of the illustratedexample, or of a volumetric region of interest 101 relative to thedifferent positions of the hand in each of the subsequent imagingsessions.

Thus, for each imaging session, the same section plane or volumetricregion of interest can be identified allowing one to carry out reliablecomparisons between the acquired image data.

FIG. 4 illustrates a variation of the method according to FIG. 3.

In this case, the same database as in FIG. 3 is used, namely a databasecomprising as input variables the parameters P(f1) to P(fn) of thefunctions f1 to fn of the mean intensity of the sequence of MRI imagesas a function of time of each of the n sample objects. The outputvariables are the uniquely correlated conditions C1 to Cn of each one ofthe sample objects.

This database is used for training and testing a predictive algorithm,such as an artificial neural network. The data of the examined objectfor which a condition CE is unknown, namely the parameters P(fE) of thefunction fE of the examined object, are provided to the trained andtested predictive algorithm and the algorithm then determines thecondition of the examined object. In this case, the determination of thecondition of the object is not carried out using a simple comparison,but instead using the more sophisticated predictive algorithm.

The fact that the method according to the invention allows one todetermine a condition of an examined object based on MRI imageacquisitions of sample objects, and the combination of the method withthe marking and registering steps and with a three-dimensional MRI imageacquisition technique, ensures that the results are highly independentfrom the variable positioning of the objects in the MRI scanner. Thisallows one to use the method to carry out follow-up examinations whichare reliable, particularly follow-up examinations of the development ofa disease with or without therapeutic treatment.

In other words, the present method allows one to identify thedevelopment, for example, of a disease, with or without a therapytreatment, which has occurred from an initial imaging session tosuccessive sessions or ones carried out at different subsequent times.

Follow-up examinations provide a very important tool for analysing thedevelopment of a disease or the response to a therapy. Generally today,MRI is considered not to be useful for follow-up examinations due to thefact that it is not simple to position the body under examinationexactly in the same position at each imaging session. This problem canbe overcome by using the method according to the present invention.

FIG. 5 illustrates the steps of a follow-up being carried out with themethod according to the present invention.

The object to be examined is subjected to an imaging session aspreviously described at different times. At each time, the condition ofthe examined body is determined with the method described above eitherby comparing the condition with a database of data relative to sampleobjects or also by simply comparing the data of the previous imagingsessions. This allows one to reconstruct a development time table of theconditions of the object.

A particular application of the above-disclosed method for determiningthe condition of an object comprises determining the disease activity ofrheumatisms and arthritis, particularly of rheumatoid arthritis. In thiscase, the object to be examined is the anatomical region of the wristand the region of interest is the synovial membrane. Indeed, it is knownthat the synovial membrane is an anatomical site where inflammation canbe detected at early stages. Thus the method according to the presentinvention can be used for determining the degree of rheumatoid arthritisactivity and for carrying out a follow-up of a patient who is affectedby rheumatoid arthritis. The database is generated by carrying out theMRI acquisition steps as described above, particularly using athree-dimensional MRI method. After one or more scout images areacquired, a region of interest centered at the synovial membrane isdefined and confirmed by the user. Anatomical and/or external markerscan be identified and or defined and also, or alternatively, externalmarkers are placed on the hand at uniquely identifiable points of themorphology of the hand. The contrast media perfusion measurements arecarried out for each patient having a known degree of activity ofrheumatoid arthritis. For each patient, the same region of interest isselected by using the markers and the registration algorithm, thusensuring that the region of interest is always identical and centered atthe synovial membrane.

After having generated the database, a patient can be subjected to aperfusion measurement carried out in exactly the same manner as theperfusion measurements of the sample patients. The same region ofinterest is selected by using the anatomical and/or external markers andthe registration algorithm. The user can also confirm the region ofinterest by visualizing the image on a screen and visually recognizingthe synovial membrane. By means of a comparison method or a predictivealgorithm, the degree of disease activity can then be determined for thepatient.

The method allows one to determine if the disease is present or not andif the disease is active or not.

Furthermore, the patient can be submitted to a follow-up observation. Ateach imaging session for contrast means perfusion measurement, the sameregion of interest is selected by using the markers as described aboveand for each perfusion measurement the degree of disease activity can bedetermined. In this way the development of the disease can be controlledand if the patient is submitted to a therapy, the success of the therapyand the progress in the disease regression can be monitored, thusallowing one to have a very strict and precise control of the diseasedevelopment and/or of the effectiveness of the therapy.

Obviously, the above method according to the invention either fordetermining the conditions of disease activity or for a diseasefollow-up can be applied to other kind of diseases, particularly todiseases which cause a variation in the vascularisation of the inflamedor attached tissue or tissues.

As for the method for determining the conditions of an object and forthe method of follow-up according to the invention, the diagnostic fieldis not the only one in which the said method can be used, but it can beapplied also to in vitro biological tissues or to non-biologicalmaterial.

Referring to the example of a solid permeable body, variation of thecondition relative to the permeation of a fluid such as a liquid or agas within the solid body can be observed and determined, for example,depending on other parameters which may vary in time, such astemperature, relative humidity, or a condition induced by other physicalor chemical treatments to which the body may be subjected.

1. A method for using magnetic resonance imaging (MRI) to evaluate aselected region of interest of an object, the method comprising:acquiring at a plurality of successive acquisition times an MRI image ofthe selected region of interest of a sample object having a knowncondition; generating for the sample object a function representing amean intensity of each of the acquired MRI images for each of thesuccessive acquisition times; acquiring at a plurality of successiveacquisition times an MRI image of the selected region of interest of atest object having an unknown condition; generating for the test objecta function representing a mean intensity of each of the acquired MRIimages for each of the successive acquisition times; and evaluating theunknown condition of the test object by comparing the generated functionfor the test object to the generated function for the sample object. 2.The method of claim 1, further comprising prior to the steps ofacquiring the MRI images of the sample object and the test object,respectively: treating the sample object to induce a response indicativeof the known condition; and treating the test object to induce aresponse indicative of the unknown condition.
 3. The method of claim 2,wherein the treating steps comprise injecting a substance into thesample object and test object, respectively.
 4. The method of claim 1,wherein the generating the sample object function and the test objectfunction steps each comprises fitting a perfusion curve to a pluralityof data points, wherein each of the data points comprises a measuredmean intensity and a corresponding one of the successive acquisitiontimes.
 5. The method of claim 4, wherein the fitting the perfusion curvestep comprises interpolating between at least two of the data points. 6.The method of claim 1, wherein the acquiring the MRI images steps eachcomprise acquiring a two-dimensional MRI image of the selected region ofinterest along a section plane of the sample object and the test object,respectively.
 7. The method of claim 1, wherein the acquiring the MRIimages steps each comprise acquiring a three-dimensional MRI image ofthe selected region of interest of the sample object and the testobject, respectively.
 8. The method of claim 7, wherein the acquiring athree-dimensional MRI image step comprises: acquiring athree-dimensional image data array; defining a section plane of thesample object and the test object, respectively; and selecting imagedata points from the three-dimensional image data array that fall on thedefined section plane.
 9. The method of claim 1, further comprisingtracking the development of the unknown condition by repeating for eachof a plurality of follow-on sessions the acquiring the MRI images of thetest object step, the generating the test object function step, and theevaluating step.
 10. The method of claim 9, wherein the tracking stepcomprises comparing the generated function for the test object for aparticular follow-on session to the generated function for the testobject for one or more previous follow-on sessions.
 11. A method forusing magnetic resonance imaging (MRI) to evaluate a selected region ofinterest of an object, the method comprising: acquiring for each of aplurality of sample objects an MRI image of the selected region ofinterest at a plurality of successive acquisition times, wherein each ofthe sample objects has an associated known condition; generating foreach of the sample objects a function representing a mean intensity ofeach of the acquired MRI images for each of the successive acquisitiontimes; approximating a parameter for each of the generated sample objectfunctions, wherein each of the approximated parameters is indicative ofa known condition of an associated one of the sample objects; acquiringfor a test object having an unknown condition an MRI image of theselected region of interest at a plurality of successive acquisitiontimes; generating for the test object a function representing a meanintensity of each of the acquired MRI images for each of the successiveacquisition times; approximating a parameter for the generated testobject function, wherein the approximated parameter is indicative of theunknown condition; and comparing the approximated parameter indicativeof the unknown condition to the approximated parameters indicative ofthe known conditions to identify the unknown condition.
 12. The methodof claim 11, further comprising providing the approximated parameterindicative of the unknown condition to a predictive algorithm developedbased on the approximated parameters indicative of the known conditions,wherein the predictive algorithm determines the unknown condition of thetest object.
 13. The method of claim 11, further comprising tracking thedevelopment of the unknown condition by repeating for each of aplurality of follow-on sessions the acquiring the MRI images of the testobject step, the generating the test object function step, and theapproximating a parameter indicative of the unknown condition step. 14.The method of claim 13, wherein the tracking step comprises comparingthe approximated parameter indicative of the unknown condition for aparticular follow-on session to the approximated parameter indicative ofthe unknown condition for one or more previous follow-on sessions. 15.The method of claim 13, further comprising using at least one of anopaque marker and an anatomical marker to identify a section plane ofthe test object for each of the follow-on sessions along which the MRIimages are acquired.
 16. The method of claim 15, further comprisingapplying a registration algorithm to spatially align the section planealong which the MRI images are acquired relative to the test object foreach of the follow-on sessions.
 17. The method of claim 16, wherein theat least one marker is used to identify a three-dimensional region ofinterest of the test object for each of the follow-on sessions, andwherein the registration algorithm is applied to spatially align thethree-dimensional region of interest of which the MRI images areacquired relative to the test object for each of the follow-on sessions.18. The method of claim 11, wherein the acquiring the MRI images foreach of the sample objects step comprises acquiring for each of thesample objects a two-dimensional MRI image along a section plane of thesample object at each of the successive acquisition times, and whereinthe acquiring the MRI images for the test object step comprisesacquiring a two-dimensional MRI image along the section plane of thetest object at each of the successive acquisition times.
 19. The methodof claim 11, wherein the acquiring the MRI images for each of the sampleobjects step comprises acquiring for each of the sample objects athree-dimensional MRI image of the selected region of interest of thesample object at each of the successive acquisition times, and whereinthe acquiring the MRI images for the test object step comprisesacquiring a three-dimensional MRI image of the selected region ofinterest of the test object at each of the successive acquisition times.20. The method of claim 19, wherein the acquiring the three-dimensionalMRI images steps comprise: acquiring a three-dimensional image dataarray; defining a section plane of the sample object and the testobject, respectively; and selecting image data points from thethree-dimensional image data array that fall on the defined sectionplane.